Django uses Python’s builtin logging module to perform system logging.
The usage of this module is discussed in detail in Python’s own documentation.
However, if you’ve never used Python’s logging framework (or even if you have),
here’s a quick primer.

A logger is the entry point into the logging system. Each logger is
a named bucket to which messages can be written for processing.

A logger is configured to have a log level. This log level describes
the severity of the messages that the logger will handle. Python
defines the following log levels:

DEBUG: Low level system information for debugging purposes

INFO: General system information

WARNING: Information describing a minor problem that has
occurred.

ERROR: Information describing a major problem that has
occurred.

CRITICAL: Information describing a critical problem that has
occurred.

Each message that is written to the logger is a Log Record. Each log
record also has a log level indicating the severity of that specific
message. A log record can also contain useful metadata that describes
the event that is being logged. This can include details such as a
stack trace or an error code.

When a message is given to the logger, the log level of the message is
compared to the log level of the logger. If the log level of the
message meets or exceeds the log level of the logger itself, the
message will undergo further processing. If it doesn’t, the message
will be ignored.

Once a logger has determined that a message needs to be processed,
it is passed to a Handler.

The handler is the engine that determines what happens to each message
in a logger. It describes a particular logging behavior, such as
writing a message to the screen, to a file, or to a network socket.

Like loggers, handlers also have a log level. If the log level of a
log record doesn’t meet or exceed the level of the handler, the
handler will ignore the message.

A logger can have multiple handlers, and each handler can have a
different log level. In this way, it is possible to provide different
forms of notification depending on the importance of a message. For
example, you could install one handler that forwards ERROR and
CRITICAL messages to a paging service, while a second handler
logs all messages (including ERROR and CRITICAL messages) to a
file for later analysis.

A filter is used to provide additional control over which log records
are passed from logger to handler.

By default, any log message that meets log level requirements will be
handled. However, by installing a filter, you can place additional
criteria on the logging process. For example, you could install a
filter that only allows ERROR messages from a particular source to
be emitted.

Filters can also be used to modify the logging record prior to being
emitted. For example, you could write a filter that downgrades
ERROR log records to WARNING records if a particular set of
criteria are met.

Filters can be installed on loggers or on handlers; multiple filters
can be used in a chain to perform multiple filtering actions.

Ultimately, a log record needs to be rendered as text. Formatters
describe the exact format of that text. A formatter usually consists
of a Python formatting string containing
LogRecord attributes; however,
you can also write custom formatters to implement specific formatting behavior.

Once you have configured your loggers, handlers, filters and
formatters, you need to place logging calls into your code. Using the
logging framework is very simple. Here’s an example:

# import the logging libraryimportlogging# Get an instance of a loggerlogger=logging.getLogger(__name__)defmy_view(request,arg1,arg):...ifbad_mojo:# Log an error messagelogger.error('Something went wrong!')

And that’s it! Every time the bad_mojo condition is activated, an
error log record will be written.

The call to logging.getLogger() obtains (creating, if
necessary) an instance of a logger. The logger instance is identified
by a name. This name is used to identify the logger for configuration
purposes.

By convention, the logger name is usually __name__, the name of
the python module that contains the logger. This allows you to filter
and handle logging calls on a per-module basis. However, if you have
some other way of organizing your logging messages, you can provide
any dot-separated name to identify your logger:

# Get an instance of a specific named loggerlogger=logging.getLogger('project.interesting.stuff')

The dotted paths of logger names define a hierarchy. The
project.interesting logger is considered to be a parent of the
project.interesting.stuff logger; the project logger
is a parent of the project.interesting logger.

Why is the hierarchy important? Well, because loggers can be set to
propagate their logging calls to their parents. In this way, you can
define a single set of handlers at the root of a logger tree, and
capture all logging calls in the subtree of loggers. A logging handler
defined in the project namespace will catch all logging messages
issued on the project.interesting and
project.interesting.stuff loggers.

This propagation can be controlled on a per-logger basis. If
you don’t want a particular logger to propagate to its parents, you
can turn off this behavior.

logging.dictConfig is a builtin library in Python 2.7. In
order to make this library available for users of earlier Python
versions, Django includes a copy as part of django.utils.log.
If you have Python 2.7 or later, the system native library will be used; if
you have Python 2.6, Django’s copy will be used.

In order to configure logging, you use LOGGING to define a
dictionary of logging settings. These settings describes the loggers,
handlers, filters and formatters that you want in your logging setup,
and the log levels and other properties that you want those components
to have.

Prior to Django 1.5, the LOGGING setting always overwrote the
default Django logging configuration.
From Django 1.5 forward, it is possible to get the project’s logging
configuration merged with Django’s defaults, hence you can decide if you want to
add to, or replace the existing configuration.

If the disable_existing_loggers key in the LOGGING dictConfig is
set to True (which is the default) the default configuration is completely
overridden. Alternatively you can redefine some or all of the loggers by
setting disable_existing_loggers to False.

Logging is configured as soon as settings have been loaded
(either manually using configure() or when at least
one setting is accessed). Since the loading of settings is one of the first
things that Django does, you can be certain that loggers are always ready for
use in your project code.

Identifies the configuration as being in ‘dictConfig version 1’
format. At present, this is the only dictConfig format version.

Disables all existing logging configurations.

Defines two formatters:

simple, that just outputs the log level name (e.g.,
DEBUG) and the log message.

The format string is a normal Python formatting string
describing the details that are to be output on each logging
line. The full list of detail that can be output can be
found in the formatter documentation.

verbose, that outputs the log level name, the log
message, plus the time, process, thread and module that
generate the log message.

Defines one filter – project.logging.SpecialFilter,
using the alias special. If this filter required additional
arguments at time of construction, they can be provided as
additional keys in the filter configuration dictionary. In this
case, the argument foo will be given a value of bar when
instantiating the SpecialFilter.

Defines three handlers:

null, a NullHandler, which will pass any DEBUG (or
higher) message to /dev/null.

console, a StreamHandler, which will print any DEBUG
(or higher) message to stderr. This handler uses the simple output
format.

mail_admins, an AdminEmailHandler, which will email any
ERROR (or higher) message to the site admins. This handler uses
the special filter.

Configures three loggers:

django, which passes all messages at INFO or higher
to the null handler.

django.request, which passes all ERROR messages to
the mail_admins handler. In addition, this logger is
marked to not propagate messages. This means that log
messages written to django.request will not be handled
by the django logger.

myproject.custom, which passes all messages at INFO
or higher that also pass the special filter to two
handlers – the console, and mail_admins. This
means that all INFO level messages (or higher) will be
printed to the console; ERROR and CRITICAL
messages will also be output via email.

If you don’t want to use Python’s dictConfig format to configure your
logger, you can specify your own configuration scheme.

The LOGGING_CONFIG setting defines the callable that will
be used to configure Django’s loggers. By default, it points at
Python’s logging.config.dictConfig() function. However, if you want to
use a different configuration process, you can use any other callable
that takes a single argument. The contents of LOGGING will
be provided as the value of that argument when logging is configured.

If you don’t want to configure logging at all (or you want to manually
configure logging using your own approach), you can set
LOGGING_CONFIG to None. This will disable the
configuration process.

Note

Setting LOGGING_CONFIG to None only means that the
configuration process is disabled, not logging itself. If you
disable the configuration process, Django will still make logging
calls, falling back to whatever default logging behavior is
defined.

Messages relating to the interaction of code with the database. For example,
every application-level SQL statement executed by a request is logged at the
DEBUG level to this logger.

Messages to this logger have the following extra context:

duration: The time taken to execute the SQL statement.

sql: The SQL statement that was executed.

params: The parameters that were used in the SQL call.

For performance reasons, SQL logging is only enabled when
settings.DEBUG is set to True, regardless of the logging
level or handlers that are installed.

This logging does not include framework-level initialization (e.g.
SETTIMEZONE) or transaction management queries (e.g. BEGIN,
COMMIT, and ROLLBACK). Turn on query logging in your database if you
wish the view all database queries.

The security loggers will receive messages on any occurrence of
SuspiciousOperation. There is a sub-logger for
each sub-type of SuspiciousOperation. The level of the log event depends on
where the exception is handled. Most occurrences are logged as a warning, while
any SuspiciousOperation that reaches the WSGI handler will be logged as an
error. For example, when an HTTP Host header is included in a request from
a client that does not match ALLOWED_HOSTS, Django will return a 400
response, and an error message will be logged to the
django.security.DisallowedHost logger.

Only the parent django.security logger is configured by default, and all
child loggers will propagate to the parent logger. The django.security
logger is configured the same as the django.request logger, and any error
events will be mailed to admins. Requests resulting in a 400 response due to
a SuspiciousOperation will not be logged to the django.request logger,
but only to the django.security logger.

To silence a particular type of SuspiciousOperation, you can override that
specific logger following this example:

This handler sends an email to the site admins for each log
message it receives.

If the log record contains a request attribute, the full details
of the request will be included in the email.

If the log record contains stack trace information, that stack
trace will be included in the email.

The include_html argument of AdminEmailHandler is used to
control whether the traceback email includes an HTML attachment
containing the full content of the debug Web page that would have been
produced if DEBUG were True. To set this value in your
configuration, include it in the handler definition for
django.utils.log.AdminEmailHandler, like this:

Note that this HTML version of the email contains a full traceback,
with names and values of local variables at each level of the stack, plus
the values of your Django settings. This information is potentially very
sensitive, and you may not want to send it over email. Consider using
something such as Sentry to get the best of both worlds – the
rich information of full tracebacks plus the security of not sending the
information over email. You may also explicitly designate certain
sensitive information to be filtered out of error reports – learn more on
Filtering error reports.

New in Django 1.6.

By setting the email_backend argument of AdminEmailHandler, the
email backend that is being used by the
handler can be overridden, like this:

This filter accepts a callback function (which should accept a single
argument, the record to be logged), and calls it for each record that passes
through the filter. Handling of that record will not proceed if the callback
returns False.

For instance, to filter out UnreadablePostError
(raised when a user cancels an upload) from the admin emails, you would
create a filter function: